The impact of ignoring measurement error when estimating sample size for epidemiologic studies
The impact of ignoring measurement error when estimating sample size for epidemiologic studies
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DOI：
10.1177/0163278703255232
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发表时间：
2003-09-01
影响因子：
2.9
通讯作者：
Devine, O
中科院分区：
文献类型：
--
作者：
Devine, O
作者提出了两个例子说明样本量估计的偏差，可以导致忽略测量误差之间的研究变量。第一个例子考察了忽略研究结果变量的错误分类对样本量估计准确性的影响。此外，作者还概述了一种简单而有效的方法来调整样本量估计值，以解释结果错误分类。在第二个例子中，作者举例说明了在使用线性回归来评估受经典测量误差影响的结果与自变量之间的关联的研究中，严重低估所需样本量的可能性。作者最后讨论了相关文献，这些文献可能有助于研究计划者对调整样本量感兴趣。大小估计，以考虑结果和预测变量的测量误差。
The author presents two examples illustrating the bias in sample-size estimates that can result from ignoring measurement error among study variables. The first example examines the impact of ignoring misclassification of the study's outcome variable on the accuracy of sample-size estimates. In addition, the author outlines a simple yet effective means of adjusting sample-size estimates to account for outcome misclassification. In the second example, the author illustrates the potential for severe underestimation of required sample size in studies using linear regression to evaluate associations between the outcome of interest and an independent variable subject to classical measurement error The author concludes with a discussion of pertinent literature that might be helpful to study planners interested in adjusting sample-size estimates to account for measurement errors in both outcome and predictor variables.